Happiness is impossible to fully measure, but by analyzing human language through different modes of communication and literature, researchers find that we possess a strong positivity bias.

via Pacific Standard:

A group of applied mathematicians and computer scientists at the University of Vermont that is fascinated with tracking happiness just published a study that quantifies how much more frequently we use happy words than sad ones. As it turns out, it’s a lot. In the recent study, the group looked at 100,000 words in 24 classic novels across 10 languages and found that we have a global, long-term tendency toward positivity.

They’re not the first people to look at a universal positivity bias. In the late 1970s two psychologists, Margaret Matlin and David Stang, put forward a theory they called the Pollyanna principle, which holds that we subconsciously lean toward positive thinking and language, even when we’re consciously focusing on negative things. Basically, our brains want to be happy. It’s a nice idea, but, since happiness is abstract, Matlin and Stang never settled on any widely accepted way to prove it.

That’s where the UVM math nerds come in. Since 2009, they’ve been looking at ways to quantify happiness, and have decided that language is the most tangible way to do so. Chris Danforth, a mathematician with a background in chaos theory, who leads the team along with Peter Dodds, whose research focuses on sociotechnical problems, says that his team is trying to measure population-scale happiness as a baseline to improve quality of life. They think happiness is just as important as GDP, or other frequently tracked measurements of well-being. They’re essentially trying to solve a social problem with a math problem. “Happiness is a hard thing to quantify,” Danforth says. “It’s quite hard to improve something you can’t measure, so we’re trying to create an instrument capable of quantifying happiness on a large scale.”

To track happiness they had to figure out what signaled the feeling and then decide how best to measure that. That ability to track emotion, which is part of a broader field called sentiment analysis, is a nut that everyone from Facebook to the National Security Agency (NSA) is trying to crack, and Dodds and Danforth believe they have found a granular way to do it.

First, they sorted out the 5,000 most commonly used words in English. They asked people to rate how positive those words were on a scale of one to nine. Subjects were given a list of words, and images of 10 stick figures, which showed a range of emotion from sad to happy, and asked to correlate them. Words like “amazing” and “summer” rated highly positive, while “terrorist” rated negative. With that information in hand they could easily track how frequently positive words showed up in large bodies of language.

Deciphering word happiness isn’t exactly straightforward because language is a moving target—“sick,” for instance, can be highly negative or positive—which is why the team looked at a large pool of words, and why drawing on books made sense for their most recent study. They needed a big sample size to draw any significant conclusions. “The math behind it is actually quite basic,” says Kameron Decker Harris, one of the grad students who worked on the project.